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A Texture-Based Method for Modeling the Background and Detecting Moving Objects
April 2006 (vol. 28 no. 4)
pp. 657-662
This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.

[1] M. Heikkilä, M. Pietikäinen, and J. Heikkilä, “A Texture-Based Method for Detecting Moving Objects,” Proc. British Machine Vision Conf., vol. 1, pp. 187-196, 2004.
[2] T. Ojala, M. Pietikäinen, and D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Feature Distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.
[3] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, July 2002.
[4] A. Prati, I. Mikic, M.M. Trivedi, and R. Cucchiara, “Detecting Moving Shadows: Algorithms and Evaluation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 918-923, July 2003.
[5] C.R. Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, “Pfinder: Real-Time Tracking of the Human Body,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, July 1997.
[6] N. Friedman and S. Russell, “Image Segmentation in Video Sequences: A Probabilistic Approach,” Proc. Conf. Uncertainty in Artificial Intelligence, pp. 175-181, 1997.
[7] C. Stauffer and W.E.L. Grimson, “Adaptive Background Mixture Models for Real-Time Tracking,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, 1999.
[8] P. KaewTraKulPong and R. Bowden, “An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection,” Proc. European Workshop Advanced Video Based Surveillance Systems, 2001.
[9] Z. Zivkovic, “Improved Adaptive Gaussian Mixture Model for Background Subtraction,” Proc. Int'l Conf. Pattern Recognition, vol. 2, pp. 28-31, 2004.
[10] Q. Zang and R. Klette, “Robust Background Subtraction and Maintenance,” Proc. Int'l Conf. Pattern Recognition, vol. 2, pp. 90-93, 2004.
[11] A. Elgammal, R. Duraiswami, D. Harwood, and L.S. Davis, “Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,” Proc. IEEE, vol. 90, no. 7, pp. 1151-1163, 2002.
[12] K. Kim, T.H. Chalidabhongse, D. Harwood, and L. Davis, “Background Modeling and Subtraction by Codebook Construction,” Proc. IEEE Int'l Conf. Image Processing, vol. 5, pp. 3061-3064, 2004.
[13] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: Principles and Practice of Background Maintenance,” Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 255-261, 1999.
[14] A. Monnet, A. Mittal, N. Paragios, and R. Visvanathan, “Background Modeling and Subtraction of Dynamic Scenes,” Proc. IEEE Int'l Conf. Computer Vision, vol. 2, pp. 1305-1312, 2003.
[15] J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-Based Segmentation Method for Traffic Monitoring Movies,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1291-1296, Sept. 2002.
[16] M. Mason and Z. Duric, “Using Histograms to Detect and Track Objects in Color Video,” Proc. Applied Imagery Pattern Recognition Workshop, pp. 154-159, 2001.
[17] S. Jabri, Z. Duric, H. Wechsler, and A. Rosenfeld, “Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information,” Proc. Int'l Conf. Pattern Recognition, vol. 4, pp. 627-630, 2000.
[18] L. Wixson, “Detecting Salient Motion by Accumulating Directionally-Consistent Flow,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 774-780, Aug. 2000.
[19] T. Matsuyama, T. Ohya, and H. Habe, “Background Subtraction for Non-Stationary Scenes,” Proc. Asian Conf. Computer Vision, pp. 622-667, 2000.

Index Terms:
Motion, texture, background subtraction, local binary pattern.
Citation:
Marko Heikkil?, Matti Pietik?inen, "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 657-662, April 2006, doi:10.1109/TPAMI.2006.68
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